fix some docs
cuicheng01
parent
3c29b0691d
commit
c48ba70c6f
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@ -158,7 +158,7 @@ git clone https://github.com/LDOUBLEV/AutoLog.git
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```
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```shell
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# 克隆 Autolog 代码库，以便获取自动化日志
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# 编译
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make -j
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```
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@ -18,6 +18,7 @@ If you only want to test speed, please refer to [The tutorial of Paddle-Lite mob
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- [2.1.1 [RECOMMEND] Use pip to install Paddle-Lite and optimize model](#2.1.1)
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- [2.1.2 Compile Paddle-Lite to generate opt tool](#2.1.2)
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- [2.1.3 Demo of get the optimized model](#2.1.3)
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- [2.1.4 Compile to get the executable file clas_system](#2.1.4)
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- [2.2 Run optimized model on Phone](#2.2)
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- [3. FAQ](#3)
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@ -40,8 +41,8 @@ For the detailed compilation directions of different development environments, p
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|Platform|Inference Library Download Link|
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|-|-|
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|Android|[arm7](https://paddlelite-data.bj.bcebos.com/Release/2.8-rc/Android/gcc/inference_lite_lib.android.armv7.gcc.c++_static.with_extra.with_cv.tar.gz) / [arm8](https://paddlelite-data.bj.bcebos.com/Release/2.8-rc/Android/gcc/inference_lite_lib.android.armv8.gcc.c++_static.with_extra.with_cv.tar.gz)|
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|iOS|[arm7](https://paddlelite-data.bj.bcebos.com/Release/2.8-rc/iOS/inference_lite_lib.ios.armv7.with_cv.with_extra.tiny_publish.tar.gz) / [arm8](https://paddlelite-data.bj.bcebos.com/Release/2.8-rc/iOS/inference_lite_lib.ios.armv8.with_cv.with_extra.tiny_publish.tar.gz)|
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|Android|[arm7](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10/inference_lite_lib.android.armv7.clang.c++_static.with_extra.with_cv.tar.gz) / [arm8](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10/inference_lite_lib.android.armv8.clang.c++_static.with_extra.with_cv.tar.gz) |
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|iOS|[arm7](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10/inference_lite_lib.ios.armv7.with_cv.with_extra.tiny_publish.tar.gz) / [arm8](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10/inference_lite_lib.ios.armv8.with_cv.with_extra.tiny_publish.tar.gz)|
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**NOTE**:
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@ -53,7 +54,7 @@ For the detailed compilation directions of different development environments, p
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The structure of the inference library is as follows:
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```
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inference_lite_lib.android.armv8/
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inference_lite_lib.android.armv8.clang.c++_static.with_extra.with_cv/
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|-- cxx C++ inference library and header files
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| |-- include C++ header files
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| | |-- paddle_api.h
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@ -148,6 +149,23 @@ paddle_lite_opt --model_file=./MobileNetV3_large_x1_0_infer/inference.pdmodel --
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```
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When the above code command is completed, there will be ``MobileNetV3_large_x1_0.nb` in the current directory, which is the converted model file.
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<a name="2.1.4"></a>
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#### 2.1.4 Compile to get the executable file clas_system
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```shell
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# Clone the Autolog repository to get automation logs
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cd PaddleClas_root_path
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cd deploy/lite/
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git clone https://github.com/LDOUBLEV/AutoLog.git
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```
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```shell
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# Compile
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make -j
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```
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After executing the `make` command, the `clas_system` executable file is generated in the current directory, which is used for Lite prediction.
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<a name="2.2"></a>
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## 2.2 Run optimized model on Phone
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@ -172,7 +190,7 @@ When the above code command is completed, there will be ``MobileNetV3_large_x1_0
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* Install ADB for windows
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If install ADB fo Windows, you need to download from Google's Android platform: [Download Link](https://developer.android.com/studio).
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First, make sure the phone is connected to the computer, turn on the `USB debugging` option of the phone, and select the `file transfer` mode. Verify whether ADB is installed successfully as follows:
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3. First, make sure the phone is connected to the computer, turn on the `USB debugging` option of the phone, and select the `file transfer` mode. Verify whether ADB is installed successfully as follows:
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```shell
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$ adb devices
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@ -183,42 +201,22 @@ When the above code command is completed, there will be ``MobileNetV3_large_x1_0
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If there is `device` output like the above, it means the installation was successful.
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4. Prepare optimized model, inference library files, test image and dictionary file used.
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4. Push the optimized model, prediction library file, test image and class map file to the phone.
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```shell
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cd PaddleClas_root_path
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cd deploy/lite/
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# prepare.sh will put the inference library files, the test image and the dictionary files in demo/cxx/clas
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sh prepare.sh /{lite inference library path}/inference_lite_lib.android.armv8
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# enter the working directory of lite demo
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cd /{lite inference library path}/inference_lite_lib.android.armv8/
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cd demo/cxx/clas/
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# copy the C++ inference dynamic library file （ie. .so) to the debug folder
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cp ../../../cxx/lib/libpaddle_light_api_shared.so ./debug/
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```shell
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adb shell mkdir -p /data/local/tmp/arm_cpu/
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adb push clas_system /data/local/tmp/arm_cpu/
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adb shell chmod +x /data/local/tmp/arm_cpu//clas_system
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adb push inference_lite_lib.android.armv8.clang.c++_static.with_extra.with_cv/cxx/lib/libpaddle_light_api_shared.so /data/local/tmp/arm_cpu/
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adb push MobileNetV3_large_x1_0.nb /data/local/tmp/arm_cpu/
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adb push config.txt /data/local/tmp/arm_cpu/
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adb push ../../ppcls/utils/imagenet1k_label_list.txt /data/local/tmp/arm_cpu/
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adb push imgs/tabby_cat.jpg /data/local/tmp/arm_cpu/
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```
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The `prepare.sh` take `PaddleClas/deploy/lite/imgs/tabby_cat.jpg` as the test image, and copy it to the `demo/cxx/clas/debug/` directory.
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You should put the model that optimized by `paddle_lite_opt` under the `demo/cxx/clas/debug/` directory. In this example, use `MobileNetV3_large_x1_0.nb` model file generated in [2.1.3](#2.1.3).
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The structure of the clas demo is as follows after the above command is completed:
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```
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demo/cxx/clas/
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|-- debug/
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| |--MobileNetV3_large_x1_0.nb class model
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| |--tabby_cat.jpg test image
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| |--imagenet1k_label_list.txt dictionary file
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| |--libpaddle_light_api_shared.so C++ .so file
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| |--config.txt config file
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|-- config.txt config file
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|-- image_classfication.cpp source code
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|-- Makefile compile file
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```
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**NOTE**:
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* `Imagenet1k_label_list.txt` is the category mapping file of the `ImageNet1k` dataset. If use a custom category, you need to replace the category mapping file.
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@ -229,33 +227,22 @@ clas_model_file ./MobileNetV3_large_x1_0.nb # path of model file
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label_path ./imagenet1k_label_list.txt # path of category mapping file
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resize_short_size 256 # the short side length after resize
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crop_size 224 # side length used for inference after cropping
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visualize 0 # whether to visualize. If you set it to 1, an image file named 'clas_result.png' will be generated in the current directory.
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num_threads 1 # The number of threads, the default is 1
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precision FP32 # Precision type, you can choose FP32 or INT8, the default is FP32
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runtime_device arm_cpu # Device type, the default is arm_cpu
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enable_benchmark 0 # Whether to enable benchmark, the default is 0
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tipc_benchmark 0 # Whether to enable tipc_benchmark, the default is 0
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```
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5. Run Model on Phone
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Execute the following command to complete the prediction on the mobile phone.
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```shell
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# run compile to get the executable file 'clas_system'
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make -j
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# move the compiled executable file to the debug folder
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mv clas_system ./debug/
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# push the debug folder to Phone
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adb push debug /data/local/tmp/
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adb shell
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cd /data/local/tmp/debug
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export LD_LIBRARY_PATH=/data/local/tmp/debug:$LD_LIBRARY_PATH
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# the usage of clas_system is as follows:
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# ./clas_system "path of config file" "path of test image"
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./clas_system ./config.txt ./tabby_cat.jpg
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adb shell 'export LD_LIBRARY_PATH=/data/local/tmp/arm_cpu/; /data/local/tmp/arm_cpu/clas_system /data/local/tmp/arm_cpu/config.txt /data/local/tmp/arm_cpu/tabby_cat.jpg'
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```
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**NOTE**: If you make changes to the code, you need to recompile and repush the `debug ` folder to the phone.
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The result is as follows:
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@ -172,7 +172,7 @@ git clone https://github.com/LDOUBLEV/AutoLog.git
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```
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```shell
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# 克隆 Autolog 代码库，以便获取自动化日志
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# 编译
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make -j
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```
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